1 Bottle sample data curation

1.1 Import/filter data

1.2 Filtered chem datasets

1.3 Coral TA stats

Generate mean, sd, sem TA for each coral

1.4 Growth dataframes

Import ta stats from above filtered data sets into growth dataframes. Create four based on different filtering of chem data.

1.5 DECIDE CUTOFF for PCO2 values

Choose which data sets to use for all figures and tables. Carb chem data excludes bottle samples with deltas >5, with precipitaiton, and with Seacarb generated pco2 values greater than the cutoff chosen above. We have chosen 2000

2 Seacarb output

Carb chem parameters generated using seacarb with measured DIC and TA values.

2.1 Coral beakers

Generate seacarb parameters for beakers with corals by treatment

2.2 Blank Beakers

Generate seacarb parameters for blank beakers by treatment

2.3 Seacarb stats by treatment

Create stats with mean, sd, and SEM for all parameters grouped by treatment (corals and empty beakers separate)

2.4 Create Seacarb Table

Export stats to carb chem table for manuscript

## # A tibble: 34 × 6
##    Type    Variable  AMB_Value ELEV_Value   HI_Value  XHI_Value
##    <chr>   <chr>         <dbl>      <dbl>      <dbl>      <dbl>
##  1 Regular n        68         61         44         33        
##  2 Blank   n        14         11         11          7        
##  3 Regular ALK_mean  0.00246    0.00367    0.00450    0.00460  
##  4 Blank   ALK_mean  0.00254    0.00374    0.00454    0.00461  
##  5 Regular ALK_SEM   0.0000116  0.0000373  0.0000743  0.000111 
##  6 Blank   ALK_SEM   0.0000271  0.000102   0.0000926  0.000150 
##  7 Regular DIC_mean  0.00212    0.00298    0.00358    0.00368  
##  8 Blank   DIC_mean  0.00219    0.00305    0.00358    0.00374  
##  9 Regular DIC_SEM   0.0000114  0.0000281  0.0000563  0.0000832
## 10 Blank   DIC_SEM   0.0000229  0.0000813  0.0000724  0.000142 
## # ℹ 24 more rows

3 Statistics

3.1 Treatment~TA ANOVA

Was alkalinity statistically different between treatments?

##             Df Sum Sq Mean Sq F value Pr(>F)    
## treatment    3 26.047   8.682     253 <2e-16 ***
## Residuals   36  1.236   0.034                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = avg_ta_mmol ~ treatment, data = grdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.47221 -0.07406 -0.00581  0.12531  0.29886 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    2.44920    0.05858   41.81  < 2e-16 ***
## treatmentELEV  1.15477    0.08285   13.94 4.46e-16 ***
## treatmentHI    1.90932    0.08285   23.05  < 2e-16 ***
## treatmentXHI   2.02485    0.08285   24.44  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1853 on 36 degrees of freedom
## Multiple R-squared:  0.9547, Adjusted R-squared:  0.9509 
## F-statistic:   253 on 3 and 36 DF,  p-value: < 2.2e-16
##  treatment emmean     SE df lower.CL upper.CL .group
##  AMB         2.45 0.0586 36     2.33     2.57  a    
##  ELEV        3.60 0.0586 36     3.49     3.72   b   
##  HI          4.36 0.0586 36     4.24     4.48    c  
##  XHI         4.47 0.0586 36     4.36     4.59    c  
## 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 4 estimates 
## significance level used: alpha = 0.05 
## NOTE: If two or more means share the same grouping symbol,
##       then we cannot show them to be different.
##       But we also did not show them to be the same.
##  contrast   estimate     SE df t.ratio p.value
##  AMB - ELEV   -1.155 0.0829 36 -13.938  <.0001
##  AMB - HI     -1.909 0.0829 36 -23.045  <.0001
##  AMB - XHI    -2.025 0.0829 36 -24.440  <.0001
##  ELEV - HI    -0.755 0.0829 36  -9.107  <.0001
##  ELEV - XHI   -0.870 0.0829 36 -10.502  <.0001
##  HI - XHI     -0.116 0.0829 36  -1.394  0.5109
## 
## P value adjustment: tukey method for comparing a family of 4 estimates

Treatment summaries

## # A tibble: 4 × 3
##   Treatment mean_ta sem_ta
##   <fct>       <dbl>  <dbl>
## 1 AMB         2467.   11.7
## 2 ELEV        3625.   32.0
## 3 HI          4355.   67.6
## 4 XHI         4530.   68.4

3.2 Calcification

3.2.1 Linear models and ANOVA

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: gr_t1t3 ~ avg_ta_mmol + (1 | geno) + (1 | tank)
##    Data: grdata
## 
## REML criterion at convergence: -15.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.61467 -0.84337  0.04106  0.74427  2.14702 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  tank     (Intercept) 0.001191 0.03451 
##  geno     (Intercept) 0.004232 0.06505 
##  Residual             0.029966 0.17311 
## Number of obs: 40, groups:  tank, 4; geno, 3
## 
## Fixed effects:
##             Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept) -0.06599    0.13419 33.35580  -0.492    0.626    
## avg_ta_mmol  0.19383    0.03334 33.71623   5.813 1.55e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## avg_ta_mmol -0.928
## Type III Analysis of Variance Table with Satterthwaite's method
##             Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)    
## avg_ta_mmol 1.0127  1.0127     1 33.716  33.795 1.55e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## gr_t1t3 ~ avg_ta_mmol + (1 | geno) + (1 | tank)
##            npar logLik     AIC     LRT Df Pr(>Chisq)
## <none>        5 7.7054 -5.4108                      
## (1 | geno)    4 7.1532 -6.3065 1.10433  1     0.2933
## (1 | tank)    4 7.6242 -7.2484 0.16239  1     0.6870
## Backward reduced random-effect table:
## 
##            Eliminated npar logLik     AIC     LRT Df Pr(>Chisq)
## <none>                   5 7.7054 -5.4108                      
## (1 | tank)          1    4 7.6242 -7.2484 0.16239  1     0.6870
## (1 | geno)          2    3 7.1071 -8.2142 1.03416  1     0.3092
## 
## Backward reduced fixed-effect table:
##             Eliminated Df Sum of Sq    RSS     AIC F value    Pr(>F)    
## avg_ta_mmol          0  1   0.99993 2.2732 -112.71  29.843 3.104e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Model found:
## gr_t1t3 ~ avg_ta_mmol

no genotype effect Simplest linear model:

## 
## Call:
## lm(formula = gr_t1t3 ~ avg_ta_mmol, data = grdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.30454 -0.15986  0.00683  0.13630  0.44281 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.05072    0.13359  -0.380    0.706    
## avg_ta_mmol  0.19144    0.03504   5.463  3.1e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.183 on 38 degrees of freedom
## Multiple R-squared:  0.4399, Adjusted R-squared:  0.4251 
## F-statistic: 29.84 on 1 and 38 DF,  p-value: 3.104e-06
## Analysis of Variance Table
## 
## Response: gr_t1t3
##             Df  Sum Sq Mean Sq F value    Pr(>F)    
## avg_ta_mmol  1 0.99993 0.99993  29.843 3.104e-06 ***
## Residuals   38 1.27324 0.03351                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "R-Squared: 0.439881998425931"
## [1] "slope: 0.191444670704968"
## [1] "p =  3.10401201218377e-06"
## [1] "intercept:  -0.050716687770756"

3.3 Linear Extension

3.3.1 Linear models and ANOVA

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: le_t1t3e ~ avg_ta_mmol + (1 | geno) + (1 | tank)
##    Data: grdata
## 
## REML criterion at convergence: -139.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.53959 -0.51073 -0.00919  0.61262  2.06220 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev.
##  tank     (Intercept) 0.000e+00 0.000000
##  geno     (Intercept) 5.267e-05 0.007258
##  Residual             1.216e-03 0.034867
## Number of obs: 40, groups:  tank, 4; geno, 3
## 
## Fixed effects:
##              Estimate Std. Error        df t value Pr(>|t|)  
## (Intercept)  0.007948   0.025904 37.126089   0.307   0.7607  
## avg_ta_mmol  0.013115   0.006690 36.763581   1.961   0.0575 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## avg_ta_mmol -0.963
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
## Type III Analysis of Variance Table with Satterthwaite's method
##                Sum Sq   Mean Sq NumDF  DenDF F value  Pr(>F)  
## avg_ta_mmol 0.0046729 0.0046729     1 36.764  3.8437 0.05755 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## ANOVA-like table for random-effects: Single term deletions
## 
## Model:
## le_t1t3e ~ avg_ta_mmol + (1 | geno) + (1 | tank)
##            npar logLik     AIC     LRT Df Pr(>Chisq)
## <none>        5 69.678 -129.36                      
## (1 | geno)    4 69.544 -131.09 0.26857  1     0.6043
## (1 | tank)    4 69.678 -131.36 0.00000  1     1.0000
## Backward reduced random-effect table:
## 
##            Eliminated npar logLik     AIC     LRT Df Pr(>Chisq)
## <none>                   5 69.678 -129.36                      
## (1 | tank)          1    4 69.678 -131.36 0.00000  1     1.0000
## (1 | geno)          2    3 69.544 -133.09 0.26857  1     0.6043
## 
## Backward reduced fixed-effect table:
##             Eliminated Df Sum of Sq      RSS     AIC F value  Pr(>F)  
## avg_ta_mmol          1  1 0.0045255 0.052141 -263.71  3.6116 0.06499 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Model found:
## le_t1t3e ~ 1

no genotype effect
Simplest linear model:

## 
## Call:
## lm(formula = le_t1t3e ~ avg_ta_mmol, data = grdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.08480 -0.02009 -0.00231  0.02505  0.07304 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.008974   0.025834   0.347    0.730  
## avg_ta_mmol 0.012879   0.006777   1.900    0.065 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0354 on 38 degrees of freedom
## Multiple R-squared:  0.08679,    Adjusted R-squared:  0.06276 
## F-statistic: 3.612 on 1 and 38 DF,  p-value: 0.06499
## Analysis of Variance Table
## 
## Response: le_t1t3e
##             Df   Sum Sq   Mean Sq F value  Pr(>F)  
## avg_ta_mmol  1 0.004525 0.0045255  3.6116 0.06499 .
## Residuals   38 0.047616 0.0012530                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = le_t1t3e ~ avg_ta_mmol, data = grdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.08480 -0.02009 -0.00231  0.02505  0.07304 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 0.008974   0.025834   0.347    0.730  
## avg_ta_mmol 0.012879   0.006777   1.900    0.065 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0354 on 38 degrees of freedom
## Multiple R-squared:  0.08679,    Adjusted R-squared:  0.06276 
## F-statistic: 3.612 on 1 and 38 DF,  p-value: 0.06499
## [1] "R-Squared: 0.0867925136177123"
## [1] "slope: 0.0128792737554567"
## [1] "p =  0.0649852461152717"
## [1] "intercept:  0.00897422202248515"

4 Figures

4.1 Assign Colors/Generate Treat Names/Label groups

4.2 Figure 1 (Treatment TA)

4.2.1 TA~treatment (Boxplot)

4.2.2 TA~time (scatter)

4.2.3 Final Figure 1 (TA_Summary_Plot)

4.3 Figure 2 (Calcification~TA)

4.3.1 stats

## 
## Call:
## lm(formula = gr_t1t3 ~ treatment, data = grdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.23435 -0.12537 -0.02452  0.12646  0.47582 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.36790    0.05408   6.803 5.96e-08 ***
## treatmentELEV  0.34706    0.07648   4.538 6.11e-05 ***
## treatmentHI    0.36982    0.07648   4.836 2.48e-05 ***
## treatmentXHI   0.45844    0.07648   5.994 7.07e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.171 on 36 degrees of freedom
## Multiple R-squared:  0.5369, Adjusted R-squared:  0.4983 
## F-statistic: 13.91 on 3 and 36 DF,  p-value: 3.516e-06
##  treatment emmean     SE df lower.CL upper.CL .group
##  AMB        0.368 0.0541 36    0.258    0.478  a    
##  ELEV       0.715 0.0541 36    0.605    0.825   b   
##  HI         0.738 0.0541 36    0.628    0.847   b   
##  XHI        0.826 0.0541 36    0.717    0.936   b   
## 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 4 estimates 
## significance level used: alpha = 0.05 
## NOTE: If two or more means share the same grouping symbol,
##       then we cannot show them to be different.
##       But we also did not show them to be the same.
##  contrast   estimate     SE df t.ratio p.value
##  AMB - ELEV  -0.3471 0.0765 36  -4.538  0.0003
##  AMB - HI    -0.3698 0.0765 36  -4.836  0.0001
##  AMB - XHI   -0.4584 0.0765 36  -5.994  <.0001
##  ELEV - HI   -0.0228 0.0765 36  -0.298  0.9907
##  ELEV - XHI  -0.1114 0.0765 36  -1.456  0.4736
##  HI - XHI    -0.0886 0.0765 36  -1.159  0.6562
## 
## P value adjustment: tukey method for comparing a family of 4 estimates
## # A tibble: 4 × 3
##   treatment max_value label
##   <fct>         <dbl> <chr>
## 1 AMB           0.649 a    
## 2 ELEV          0.870 b    
## 3 HI            0.87  b    
## 4 XHI           1.09  b

4.3.2 calcification~treatment (boxplot)

4.3.3 calcification~TA (regression)

## # A tibble: 4 × 3
##   treatment mean_value    sem
##   <fct>          <dbl>  <dbl>
## 1 AMB            0.368 0.0519
## 2 ELEV           0.715 0.0337
## 3 HI             0.738 0.0646
## 4 XHI            0.826 0.0608
## [1] 124.6089

4.3.4 Final figure 2 (Calcification_Summary_Plot)

4.4 Figure 3 (linear extension~TA)

4.4.1 stats

## 
## Call:
## lm(formula = le_t1t3e ~ avg_ta, data = growth_2000)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.08480 -0.02009 -0.00231  0.02505  0.07304 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) 8.974e-03  2.583e-02   0.347    0.730  
## avg_ta      1.288e-05  6.777e-06   1.900    0.065 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0354 on 38 degrees of freedom
## Multiple R-squared:  0.08679,    Adjusted R-squared:  0.06276 
## F-statistic: 3.612 on 1 and 38 DF,  p-value: 0.06499
## 
## Call:
## lm(formula = le_t1t3e ~ treatment, data = growth_all)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.086972 -0.020711 -0.004481  0.026055  0.073910 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    0.03658    0.01132   3.230  0.00264 **
## treatmentELEV  0.02434    0.01601   1.520  0.13723   
## treatmentHI    0.02423    0.01601   1.513  0.13895   
## treatmentXHI   0.03272    0.01601   2.043  0.04839 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.03581 on 36 degrees of freedom
## Multiple R-squared:  0.1147, Adjusted R-squared:  0.04095 
## F-statistic: 1.555 on 3 and 36 DF,  p-value: 0.2171
##  treatment emmean     SE df lower.CL upper.CL .group
##  AMB       0.0366 0.0113 36   0.0136   0.0595  a    
##  HI        0.0608 0.0113 36   0.0378   0.0838  a    
##  ELEV      0.0609 0.0113 36   0.0380   0.0839  a    
##  XHI       0.0693 0.0113 36   0.0463   0.0923  a    
## 
## Confidence level used: 0.95 
## P value adjustment: tukey method for comparing a family of 4 estimates 
## significance level used: alpha = 0.05 
## NOTE: If two or more means share the same grouping symbol,
##       then we cannot show them to be different.
##       But we also did not show them to be the same.

Calculate percent increase between controls and highest treatment

## # A tibble: 4 × 3
##   treatment mean_value     sem
##   <fct>          <dbl>   <dbl>
## 1 AMB           0.0366 0.00881
## 2 ELEV          0.0609 0.0118 
## 3 HI            0.0608 0.00949
## 4 XHI           0.0693 0.0144
## [1] 89.45448

4.4.2 LE~treatment (boxplot)

4.4.3 LE~TA (regression)

4.4.4 Final figure 3

4.5 Growth across timepoints – exploration

growth across timepoints.

4.5.1 growth~first half (regression)

growth (calcificaiton and linear extension) from timepoint 1 to timepoint 2

4.5.1.1 stats for t1-t2
## 
## Call:
## lm(formula = le_t1t2e ~ avg_ta_mmol, data = growth_t1t2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10099 -0.04062 -0.00082  0.03377  0.12879 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.02205    0.04053   0.544   0.5897  
## avg_ta_mmol  0.02809    0.01071   2.623   0.0126 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0562 on 37 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1568, Adjusted R-squared:  0.134 
## F-statistic: 6.881 on 1 and 37 DF,  p-value: 0.01258
## Analysis of Variance Table
## 
## Response: le_t1t2e
##             Df   Sum Sq   Mean Sq F value  Pr(>F)  
## avg_ta_mmol  1 0.021737 0.0217367  6.8815 0.01258 *
## Residuals   37 0.116873 0.0031587                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = le_t1t2e ~ avg_ta_mmol, data = growth_t1t2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.10099 -0.04062 -0.00082  0.03377  0.12879 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.02205    0.04053   0.544   0.5897  
## avg_ta_mmol  0.02809    0.01071   2.623   0.0126 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0562 on 37 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.1568, Adjusted R-squared:  0.134 
## F-statistic: 6.881 on 1 and 37 DF,  p-value: 0.01258
## 
## Call:
## lm(formula = gr_t1t2 ~ avg_ta_mmol, data = growth_t1t2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.52003 -0.14493 -0.00234  0.14115  0.52697 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11809    0.16899   0.699 0.489066    
## avg_ta_mmol  0.18638    0.04465   4.174 0.000174 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2343 on 37 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3202, Adjusted R-squared:  0.3018 
## F-statistic: 17.42 on 1 and 37 DF,  p-value: 0.000174
## Analysis of Variance Table
## 
## Response: gr_t1t2
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## avg_ta_mmol  1 0.9568 0.95680  17.425 0.000174 ***
## Residuals   37 2.0317 0.05491                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Percenet increase in LE from t1 to t2

## # A tibble: 4 × 3
##   treatment mean_value    sem
##   <fct>          <dbl>  <dbl>
## 1 AMB           0.0784 0.0136
## 2 ELEV          0.134  0.0190
## 3 HI            0.135  0.0164
## 4 XHI           0.155  0.0190
## [1] 97.63354
4.5.1.2 regression plot

4.5.2 growth second half (regression)

4.5.2.1 stats
## 
## Call:
## lm(formula = le_t2t3e ~ avg_ta_mmol, data = growth_t2t3)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.30615 -0.02895  0.02564  0.05379  0.11747 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01345    0.07259   0.185    0.854
## avg_ta_mmol -0.01844    0.01949  -0.946    0.352
## 
## Residual standard error: 0.0932 on 28 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.03099,    Adjusted R-squared:  -0.003618 
## F-statistic: 0.8955 on 1 and 28 DF,  p-value: 0.3521
## Analysis of Variance Table
## 
## Response: le_t2t3e
##             Df   Sum Sq   Mean Sq F value Pr(>F)
## avg_ta_mmol  1 0.007779 0.0077789  0.8955 0.3521
## Residuals   28 0.243237 0.0086870
## 
## Call:
## lm(formula = le_t2t3e ~ avg_ta_mmol, data = growth_t2t3)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.30615 -0.02895  0.02564  0.05379  0.11747 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01345    0.07259   0.185    0.854
## avg_ta_mmol -0.01844    0.01949  -0.946    0.352
## 
## Residual standard error: 0.0932 on 28 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.03099,    Adjusted R-squared:  -0.003618 
## F-statistic: 0.8955 on 1 and 28 DF,  p-value: 0.3521
## 
## Call:
## lm(formula = gr_t2t3 ~ avg_ta_mmol, data = growth_t2t3)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6572 -0.1016 -0.0188  0.1690  0.4270 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.23906    0.17527  -1.364 0.183441    
## avg_ta_mmol  0.18002    0.04706   3.826 0.000669 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.225 on 28 degrees of freedom
##   (10 observations deleted due to missingness)
## Multiple R-squared:  0.3433, Adjusted R-squared:  0.3198 
## F-statistic: 14.64 on 1 and 28 DF,  p-value: 0.0006692
## Analysis of Variance Table
## 
## Response: gr_t2t3
##             Df Sum Sq Mean Sq F value    Pr(>F)    
## avg_ta_mmol  1 0.7412 0.74120  14.636 0.0006692 ***
## Residuals   28 1.4180 0.05064                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
4.5.2.2 regression

4.6 Figure 4 (growth across timepoints)

4.7 Supplementary Figure 1 (Property-Property Plot)

4.7.1 Data from your T1

Ideal and other modeled data assumes pCO2 of 400 and the +Alk targets with the 1.26:1, 1:0, and 0:1 to compare methods with keeping Alk targets and letting DIC and pCO2 vary. +0 (2400), Ideal: 2068.82 Na2CO3 Only: 2068.82 NaHCO3 Only: 2068.82

+15 (3900), Ideal: 3243.677 Na2CO3 Only: 2818.82 NaHCO3 Only: 3568.82

+30 (5400), Ideal: 4369.416 Na2CO3 Only: 3568.82 NaHCO3 Only: 5068.82

+45 (6900) Ideal: 5459.844 Na2CO3 Only: 4318.82 NaHCO3 Only: 6568.82

##   treatment             type                sal              pH       
##  Length:16          Length:16          Min.   :34.24   Min.   :7.710  
##  Class :character   Class :character   1st Qu.:34.39   1st Qu.:8.059  
##  Mode  :character   Mode  :character   Median :34.39   Median :8.260  
##                                        Mean   :34.39   Mean   :8.249  
##                                        3rd Qu.:34.39   3rd Qu.:8.400  
##                                        Max.   :34.60   Max.   :8.968  
##       pCO2              DIC            HCO3           CO3        
##  Min.   :  56.69   Min.   :2069   Min.   :1819   Min.   : 239.0  
##  1st Qu.: 313.32   1st Qu.:2660   1st Qu.:1949   1st Qu.: 280.1  
##  Median : 400.00   Median :3569   Median :2597   Median : 540.3  
##  Mean   : 630.12   Mean   :3609   Mean   :2886   Mean   : 705.7  
##  3rd Qu.: 400.66   3rd Qu.:4331   3rd Qu.:3311   3rd Qu.: 859.1  
##  Max.   :3011.36   Max.   :6569   Max.   :6127   Max.   :2227.0  
##       ALK       OmegaAragonite  
##  Min.   :2400   Min.   : 3.846  
##  1st Qu.:3444   1st Qu.: 4.506  
##  Median :4220   Median : 8.698  
##  Mean   :4452   Mean   :11.355  
##  3rd Qu.:5400   3rd Qu.:13.825  
##  Max.   :6900   Max.   :35.835

4.7.2 create a range of carb chems surrounding your data

4.7.3 Plot the range, overlay your data

Calculate percent increase in growth between controls and different treatments

## # A tibble: 4 × 3
##   treatment mean_value    sem
##   <fct>          <dbl>  <dbl>
## 1 AMB           0.0784 0.0136
## 2 ELEV          0.134  0.0190
## 3 HI            0.135  0.0164
## 4 XHI           0.155  0.0190
## [1] 97.63354
## # A tibble: 4 × 3
##   treatment mean_value    sem
##   <fct>          <dbl>  <dbl>
## 1 AMB            0.512 0.0569
## 2 ELEV           0.880 0.0494
## 3 HI             0.887 0.0821
## 4 XHI            0.991 0.0924
## [1] 93.28842

5 Call Final Figures